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16036-01 - Lecture: Regression and Statistical Learning (3 CP)
| Semester | spring semester 2025 |
| Course frequency | Every spring sem. |
| Lecturers | Christian Kleiber (christian.kleiber@unibas.ch, Assessor) |
| Content | Introductory econometrics courses mainly cover the linear regression model, which is suitable for modelling response variables that may be considered as continuous. Also, the number of covariates is typically modest. The present course has two parts: * In the first part, the course will cover classical (nonlinear) regression models for applications where response variables are naturally discrete, e.g. binary or count data. It will use the framework of generalized linear models (GLMs), which provides a unified approach to models such as logit, probit and Poisson regression. Inference will be likelihood based. * In the second part, there will be an introduction to the recent literature on statistical learning (aka machine learning), specifically to the notion of regularisation, with LASSO and ridge as the main examples, and mainly in the setting of linear regression. If time permits there will also be material on finite mixture models and/or generalized additive models (GAMs). Remarks: * This is a somewhat modified version of a course formerly titled "Microeconometrics and Statistical Learning". The motivation is that the content is not 'just' for economics majors -- it is for all students who want to study regression methods beyond the linear regression model. * Software / programming language: R, see https://www.R-project.org/. Basic knowledge of R is expected. * Empirical illustrations may include data from health economics, insurance, or labor economics, among further sources. * In order to make room for further (regression) models, there will at most be a brief review of likelihood methods, possibly offered in digital form. Participants are expected to be familiar with these methods at the level of the compulsory MSc level Econometrics course. |
| Learning objectives | * Regression beyond the linear regression model: (more on) binary response, counts and extensions. * Basics of generalized linear models (GLMs), possibly also of generalized additive models (GAMs). * Basics of modern, regularized estimators that appear in statistical / machine learning. * Application of the methods using a modern statistical computing environment. |
| Bibliography | Main references: Cameron AC, Trivedi PK (2005). Microeconometrics, Cambridge Univ. Press. Fahrmeir L, Kneib T, Lang S, Marx B (2013). Regression. Springer. James G, Witten D, Hastie T, Tibshirani R (2021). An Introduction to Statistical Learning, 2nd ed. New York: Springer. [available in electronic form via the university library!] Winkelmann R, Boes S (2009). Analysis of Microdata, 2nd ed, Springer. Other (topic-specific) references will be given in the appropriate contexts. |
| Comments | All course materials are on OLAT ... not on ADAM! |
| Weblink | Weblink |
| Admission requirements | Prerequisites: For students from Master's programmes of the Faculty of Business and Economics: * Completed Bachelor's degree. * Econometrics [MSc] For students from other departments: * Regression basics. * A second course in statistics, notably covering likelihood methods. |
| Course application | Registration: Please enroll in the Online Services (services.unibas.ch); Eucor-Students and mobility students of other Swiss Universities or the FHNW first have to register at the University of Basel BEFORE the start of the course and receive their login data by post (e-mail address of the University of Basel). Processing time up to a week! Detailed information can be found here: https://www.unibas.ch/de/Studium/Mobilitaet.html After successful registration you can enroll for the course in the Online Services (services.unibas.ch). Applies to everyone: Enrolment = Registration for the course and the exam! |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Course auditors welcome |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Wednesday | 10.15-12.00 | Wirtschaftswissenschaftliche Fakultät, Seminarraum S15 HG.31 |
Dates
| Modules |
Module: Core Competences in Economics (Master's Studies: Sustainable Development) Module: Field Electives in Economics and Public Policy (Master's Studies: Economics and Public Policy) Module: Non-Life Insurance (Master's Studies: Actuarial Science) Module: Preparation Master's Thesis in Economics (Master's Studies: Sustainable Development) Module: Specific Electives in Data Science and Computational Economics (Master's Studies: Business and Economics) Module: Specific Electives in Economics (Master's Studies: Business and Economics) Module: Specific Electives in Marketing and Strategic Management (Master's Studies: Business and Economics) Module: Statistics and Computational Science (Master's Studies: Actuarial Science) Module: Technology Field (Master's Studies: Business and Technology) Specialization Module: Areas of Specialization in International and/or Monetary Economics (Master's Studies: International and Monetary Economics) |
| Assessment format | record of achievement |
| Assessment details | Notes for the Assessment: Written exam: date and room tbd In addition, there will be several assignments, accounting for up to 30% of the total grade. For the assignments, students may work in groups of two. |
| Assessment registration/deregistration | Reg.: course registration, dereg: cancel course registration |
| Repeat examination | no repeat examination |
| Scale | 1-6 0,1 |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Business and Economics , studiendekanat-wwz@unibas.ch |
| Offered by | Faculty of Business and Economics |